GPC2022

Acronym : EIEECD

General Information :

The continuous evaluation of networking technologies has led the number of edge devices (e.g., sensors, smart phones, actuators, e.g.) to be growing at an unprecedented pace. These edge devices generate an excessive amount of data, and traditionally they have to transfer the collected data through the backbone networks to data centers for further computing, storing, and analyzing. However, the long delivery path introduces unignorable latency which significantly degrades the user experience. To reduce the data transmission latency, edge servers have been deployed at the network edge for replacing the far away data center servers. However, running those edge servers also consumes tremendous energy. In addition, since the edge devices are battery-powered in general, they also have to face the energy efficiency issue. Therefore, how to efficiently transfer the excessive amount of collected data from the edge devices to the edge servers, and reduce the energy consumption for both of them has been a crucial challenge. Compared to the traditional approaches, machine learning (ML) or artificial intelligence (AI) technologies are believed to be more promising for solving the above challenges.
Thus, GPC 2022 Workshop on Edge Intelligence for Energy Efficient Content Delivery (EIEECD) seeks to bring together researchers and experts from academia, industry, and government agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic. Suitable topics for this workshop include, but are not limited to, the following areas:

• Wireless network optimization for improving the performance of edge computing
• Self-adaptive energy-aware routing algorithms
• AI-based energy efficient edge caching algorithms
• Energy-aware efficient ML/AI algorithms for edge devices
• Offloading and scheduling strategy for edge intelligence
• Architecture and applications of edge intelligence for IoT
• AI-based traffic engineering for edge networks
• Reinforcement learning for network decision making, network control, and management
• Multi-agent deep reinforcement learning for energy aware resource allocation
• Federated learning for edge networks
• Blockchain for edge intelligence
• Energy Efficient resource allocation in edge networks

Organizer Information :

Ning Zhang, University of Windsor, Email: ning.zhang@uwindsor.ca
Zhicai Zhang, Shanxi University, Email: zzcai@sxu.edu.cn
Zhe Zhang, Nanjing University of Posts and Telecommunications, Email: zhezhang@njupt.edu.cn

Acronym : AICEC

General Information :

Food (meat, milk and cereals) security remains a global challenge due to global population increasing, climate change and war conflicts. Smart farming paves a way to increase the crop and livestock production efficiency use the limited sources (e.g. soil, water and fertilizer). Smart farming integrates varieties of sensor data, environmental information and hardware technologies to improve sustainability and increase yield. Artificial Intelligence and Edge Computing provides a solution enables smart farming decision more effectively by process the hierarchical data, complex information, access and utilize smart agriculture devices. Some farming related applications such as soil quality examination, crop health analysis, livestock monitoring and corn yield estimation can be achieved in an efficient way by using artificial intelligence and edge computing. The objective of this special issue is to report the state-of-the-art research and progress in the applications of artificial intelligence and edge computing for smart farming. We seek original papers with novel research contributions in all aspects. Topics of interest mainly include:

• Artificial Intelligence
• Edge computing
• Cloud computation
• Intelligent equipment
• Crop farming
• Livestock farming
• Internet of things
• UAV or Robot
• Remote Sensing
• Smart agriculture
• Smart sensors and perception

Organizer Information :

Yongliang Qiao, The University of Sydney, yongliang.qiao@ieee.org
Daobilige Su, China Agricultural University, sudao@cau.edu.cn
Zichen Huang, Kyoto University, huang.zichen.22c@kyoto-u.jp Yangyang Guo, Anhui University, guoyangyang113529@ahu.edu.cn
Qiankun Fu, Jilin University, qkfu@jlu.edu.cn
Honghua Jiang, Shandong Agricultural University, j_honghua@sdau.edu.cn
Meili Wang, Northwest A&F University, wml@nwsuaf.edu.cn

Acronym : AIICAV

General Information :

Connected and Autonomous Vehicles (CAVs) can utilize communication services among close vehicles (V2V) and roadside infrastructure (V2I), which have great potential and capacity to realize safer and more efficient transportation by enabling traffic flow thoroughly monitored and understood. The rise and achievements of CAVs have been witnessed in the past decades and drawn more and more attentions from academia, government and industry. However, CAVs still face correspondingly technological challenges to perform more “safe, efficient and environment friendly”. As a consequence, the number of commercial CAVs on public road network is still limited. The development heavily lies on the breakthrough of intelligent industrial informatics technologies, such as edge computing, federal computing, communication security, cyber-physical systems, blockchain, and so on, related to sensing, planning, decision-making, control and evaluation of CAVs.
The objective of this workshop is to bring together researcher and practitioners in academia, industry and government to present and discuss their latest research findings and engineering experiences of advanced industrial informatics for connected and autonomous vehicles. This workshop will focus on (but not limited to) the following topics:

• Federal computing for CAV
• Edge computing for CAV
• Vehicular cyber-physical systems
• Deep reinforcement learning for CAV
• Path planning and movement control for CAV
• Machine learning for CAV
• Big data analytics for CAV
• V2X communication
• Blockchain for CAV
• Energy consumption, efficiency and environment research for CAV
• HMI and driving behavior research for CAV
• Travel demand prediction and analysis for CAV

Organizer Information :

Fenghua Zhu, Institute of Automation, Chinese Academy of Sciences, fenghua.zhu@ia.ac.cn
Ting Xu, Chang'an University, xuting@chd.edu.cn
Ryan Wen Liu, Wuhan University of Technology, wenliu@whut.edu.cn
Rummei Li, Beijing Jiaotong University, rmli@bjtu.edu.cn

Acronym : AIIoT

General Information :

With the large-scale application of the Internet of Things (IoT), IoT devices and applications will generate a large amount of data, and these IoT nodes are different in terms of computing ability, storage capacity, traffic type, battery resource, etc., posing huge challenges to IoT resource management, traffic scheduling, privacy, and security, etc. Using traditional algorithms and strategies has been unable to meet the requirements of the IoT. Modern applications of IoT systems must process and analyze big data with the help of powerful artificial intelligence (AI) technologies to find the best solutions and make the best decisions. Modern AI techniques typically utilize evolutionary computing, nature-inspired algorithms, machine learning, or deep learning for event monitoring and optimal decision-making. The fusion of IoT systems and AI technologies is very suitable for the augmented intelligence of IoT, such as autonomous driving, smart grid, etc.
This workshop calls for original research articles focusing on new insights, new algorithms, and new applications of AI-Based IoT (including Internet of Vehicles, Smart Grid, Smart City, Smart Home, etc.). Potential topics include but are not limited to the following:

• AI-based IoT architecture
• Communication protocols in AI-Based IoT
• Security and privacy for AI-Based IoT
• IoT innovative applications and services based on artificial intelligence
• Location awareness in AI-based IoT
• Machine learning and deep learning in AI-based IoT
• Computational intelligence algorithms in AI-based IoT
• Event monitoring in AI-Based IoT

Organizer Information :

Tigang Jiang, University of Electronic & Science Technology of China, jtg@uestc.edu.cn
Shaoen Wu, Illinois State University, swu1235@ilstu.edu
Qing Yang, University of North Texas, Qing.Yang@unt.edu
Kun Hua, Lawrence Technological University, khua@ltu.edu

Acronym : SISIoT

General Information :

Internet of Things (IoT) as a new emerging and fast-growing technology has attracted lots of attention worldwide recently. The successful application of the IoT has greatly facilitated people's production activities, such as Urban construction, industrial production, information services, underwater exploration, etc. Due to the unique network architecture and ubiquitous connectivity of the IoT, it faces problems such as resource allocation, security, and crowdsensing. The emergence and development of advanced technologies such as artificial intelligence, big data, blockchain, edge computing, and cryptography have provided new opportunities for the intelligence and sustainability of the IoT. We invite submissions on the research topic Sustainable and Intelligent Solutions for the Internet of Things, covering both theoretical and systems studies.
The objective of this workshop is to provide a timely venue for researchers and industry partners in the IoT field to present and discuss their latest findings and results in sustainable and smart IoT related work. The topics include but are not limited to:

• 5G and beyond networks
• Artificial Intelligence (AI) and machine learning (ML) for IoT applications
• Big data and machine learning for IoT networks
• Blockchain-based security for IoT
• Cloud computing/mobile cloud computing
• Challenged Environments (underwater, underground)
• Connected unmanned aerial/terrestrial/underwater systems
• Crowdsourcing
• Federated learning (FL) and Deep learning (DL) for IoT
• Energy efficiency in networks
• Edge and fog computing/networking
• Fault tolerance, reliability and survivability
• Information security and privacy
• Internet of Things
• IoT Data security
• Mobile sensing and applications
• Mobility management and models
• Privacy-preserving FL/DL algorithm for IoT
• Smart grid applications
• Social computing and networks
• Software-defined networking
• Vehicular networks
• Trust management for IoT

Organizer Information :

Guangjie Han, Hohai University, hanguangjie@ieee.org
Kai Lin, Dalian University of Technology, link@dlut.edu.cn

Acronym : CER

General Information :

The theme of this workshop is the Cognitive Computing on Education and Research. The general impression of traditional computing in education and research is mainly to build logistic services, which is far from meeting the requirements for intelligent services in education and research. In the past decade, artificial intelligence has a steady progress in cognitive computing. This leads researchers to develop the advance services that build on the cognitive computing for education and research.
Previous studies have shown that cognitive computing, such as geometry theorem provers, problem solving and research knowledge mining, can be applied to enhance many services in education and research. Hence, we have a basis to organize this workshop. The topics of the workshop include but not limited to:

• Intelligent Tutoring Systems
• Services on Cloud on Education and Research
• Service Robots for Education and Research
• Reasoning Technology on Education and Research
• Metaverse on Education and Research
• Reasoning Based Learning Diagnosis
• Reasoning Enhanced Research Methodology
• Digital Human Enhanced Teaching

Organizer Information :

Contact info: email: 2429346468@qq.com; HP in China: 15527512658.
Xinguo Yu, Central China Normal University
Jiehan Zhou, University of Oulu
Jun Shen, Southeast University
Wenbin Gan, National Institute of Information and Communications Technology (NICT)

Acronym : GCEI-IIM

General Information :

As the industrial Internet deeply integrated with manufacturing, the drive capability of industrial intelligence becomes prominent regarding the digitization and informatization of the manufacturing industry. Edge intelligence, which can alleviate bandwidth transmission pressure, shorten service response delay, and protect the security of private data, provides a possible approach to satisfy the performance requirements in industrial intelligence applications. How to make efficient use of all computing and storage resources on the terminal-edge-cloud path through collaboration, comprehensively improve data processing capacity, effectively reduce network overhead and system resource consumption, is of great significance to promote the implementation and promotion of industrial intelligence applications. The topics of the workshop include but not limited to:

• Energy-efficient Internet of Things technology
• Intelligent control theory and method
• Intelligent computing and machine learning
• Information processing and control of unmanned systems
• Industrial Internet architecture, theory and method
• Process industry intelligent optimization manufacturing

Organizer Information :

Keke Huang, Professor Central South University Changsha, huangkeke@csu.edu.cn